ABSTRACT
Increasing demand for automation is being observed especially during the recent scenarios like the Covid-19 pandemic, wherein direct contact of the healthcare workers with the patients can be life-threatening. The use of robotic manipulators facilitates in minimizing such risky interactions and thereby providing a safe environment. In this research work, a single link robotic manipulator (SLRM) system is taken, which is a nonlinear multi–input–multi–output system. In order to address the limitations like heavy object movements, uncontrolled oscillations in positional movement, and improper link variations, an adaptive fractional-order nonlinear proportional, integral, and derivative (FONPID) controller has been suggested. This aids in the effective trajectory tracking of the performance of the SLRM system under step input response. Further, by tuning the controller gains using genetic algorithm optimization (GA) based on the minimum objective function (JIAE ) of the integral of absolute error (IAE) index, the suggested controller has been made more robust for trajectory tracking performance. Finally, the comparative analysis of the simulation results of proportional & integral (PI), proportional, integral, & derivative (PID), fractional-order proportional, integral, & derivative (FOPID), and the suggested FONPID controllers validated that the FONPID controller has performed better in terms of minimum JIAE and lower oscillation amplitude in trajectory tracking of positional movement of SLRM system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Solid waste management is one of the critical challenges seen everywhere, and the coronavirus disease (COVID-19) pandemic has only worsened the problems in the safe disposal of infectious waste. This paper outlines a design for a mobile robot that will intelligently identify, grasp, and collect a group of medical waste items using a six-degree of freedom (DoF) arm, You Only Look Once (YOLO) neural network, and a grasping algorithm. Various designs are generated before running simulations on the selected virtual model using Robot Operating System (ROS) and Gazebo simulator. A lidar sensor is also used to map the robot's surroundings and navigate autonomously. The robot has good scope for waste collection in medical facilities, where it can help create a safer environment.
ABSTRACT
Solid waste management is one of the critical challenges seen everywhere, and the coronavirus disease (COVID-19) pandemic has only worsened the problems in the safe disposal of infectious waste. This paper outlines a design for a mobile robot that will intelligently identify, grasp, and collect a group of medical waste items using a six-degree of freedom (DoF) arm, You Only Look Once (YOLO) neural network, and a grasping algorithm. Various designs are generated before running simulations on the selected virtual model using Robot Operating System (ROS) and Gazebo simulator. A lidar sensor is also used to map the robot's surroundings and navigate autonomously. The robot has good scope for waste collection in medical facilities, where it can help create a safer environment.
ABSTRACT
Automation of processes through the use of industrial robots is a critical component of the transition to Industry 4.0. This paper aims to present the design of a biomedical robotic manipulator and attempt to simulate its trajectory in a virtual environment. The task of collecting samples for COVID-19 serves as a case study for the manipulator. Using the CAD tool, a suitable design was developed to meet the task requirements. After determining the end effector waypoints, path planning was carried out. Following that, a cubic polynomial trajectory was implemented in the MATLAB environment to obtain the time-scheduled third-order trajectories of the robot joints. Finally, the trajectory optimization algorithm based on the concept of via-points was developed to reduce the energy consumed by the robot while performing the task. The results from the optimization algorithm showed the energy savings of approximately 28% by following the optimized trajectory. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
A novel coronavirus disease (COVID-19) is transmitting throughout the globe. During this Pandemic situation, medical robots are playing an important role in protecting front line medical staff from this disease. The flexible robotic manipulator has mechanical flexibility, due to that fluctuation or oscillations can be seen either during or after the movement of a manipulator and can create uncertainty in medical operations. During this pandemic situation, reliable operations of these robots are necessary that depend upon the stability of flexible manipulators. In this article, Linear Quadratic Regulator (LQR), Pole Placement, and Proportional-Integral-Derivatives (PID) control methods have been used to investigate the robust control method for controlling the position of manipulator with flexible link in medical robots. To carry out this research, an effective variant of the flexible Link robotic manipulator has been used as a framework to analyze the robust control method. The Matlab®/Simulink result shows that the LQR control method provides better control response compared to PID and pole placement method and thus provides reliable operation to Medical Robots.